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State Estimation for Drone Racing
State Estimation for Drone Racing
In drone racing, human pilots navigate quadrotor drones as fast as possible through a sequence of gates. To achieve the performances of world-class pilots with an autonomous system, the main challenge is to estimate the drone’s state from onboard sensors accurately. This project aims to implement a state estimation pipeline for autonomous racing on real hardware.
In drone racing, human pilots navigate quadrotor drones as fast as possible through a sequence of gates. To achieve the performances of world-class pilots with an autonomous system, the main challenge is to estimate the drone’s state from onboard sensors accurately. This project aims to implement a state estimation pipeline for autonomous racing on real hardware.
This project has three goals: First, extend an existing communication protocol (ROS) with sensor readings from the low-level flight controller (Betaflight). Second, integrate the sensor readings in an existing state estimation pipeline and validate its performance against ground truth data (real-world motion capture). Third, demonstrate state estimation for closed-loop control using an available flight stack.
This project has three goals: First, extend an existing communication protocol (ROS) with sensor readings from the low-level flight controller (Betaflight). Second, integrate the sensor readings in an existing state estimation pipeline and validate its performance against ground truth data (real-world motion capture). Third, demonstrate state estimation for closed-loop control using an available flight stack.
Please send your CV and transcripts (bachelor and master) to Christian Pfeiffer (cpfeiffe AT ifi DOT uzh DOT ch) and Giovanni Cioffi (cioffi AT ifi DOT uzh DOT ch).
Please send your CV and transcripts (bachelor and master) to Christian Pfeiffer (cpfeiffe AT ifi DOT uzh DOT ch) and Giovanni Cioffi (cioffi AT ifi DOT uzh DOT ch).